Numeric ratings tell us students are satisfied—but not why, or where we're losing them. This presentation shows how adding open-ended comment fields to tutorial evaluations and applying sentiment analysis uncovered patterns in student frustration, confidence, and engagement that scores alone missed entirely. Drawing on over 4,000 student responses, I'll walk through practical approaches to collecting and analyzing qualitative feedback using natural language processing techniques. Specific examples illustrate how clusters of negative sentiment revealed gaps in research instruction, while positive patterns pointed to what was actually working. Attendees will leave with applicable strategies for implementing comment collection, conducting sentiment analysis on library feedback, and translating findings into concrete tutorial improvements. Relevant for instruction librarians, assessment coordinators, and anyone working to strengthen information literacy programs through student voice.
Participants will: 1. be able to identify low-cost feedback systems that they can embed in their own tutorials. 2. be able to describe the types of feedback students leave and how they can be used to improve tutorial design.
